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Risk factors and a predictive model for the development of epilepsy after Japanese encephalitis
Dou-Dou Chen1, Xiao-Ling Peng2, Huan Cheng3
1Department of Neurology, Children's Hospital of Chongqing Medical University, China; Ministry of Education Key Laboratory of Child Development and Disorders, China; National Clinical Research Center for Child Health and Disorders (Chongqing) China; China International Science and Technology Cooperation base of Child development and Critical Disorders China; Chongqing Key Laboratory of Pediatrics China.
Insights
Seizures are common in children with Japanese encephalitis (JE). A new nomogram predicts post-encephalitic epilepsy (PEE) risk, aiding early intervention for better outcomes in pediatric JE patients.
Area of Science:
- Pediatric Neurology
- Infectious Diseases
- Epileptology
Background:
- Japanese encephalitis (JE) is a significant cause of neurological sequelae in children.
- Seizures are a frequent complication during the acute phase of JE.
- Post-encephalitic epilepsy (PEE) poses a long-term challenge for affected children.
Purpose of the Study:
- To characterize seizure patterns in acute JE.
- To identify risk factors for developing PEE.
- To develop and validate a predictive model and nomogram for PEE risk.
Main Methods:
- Retrospective analysis of 328 children with JE.
- Univariable and multivariable logistic regression for risk factor identification.
- Development and validation of a predictive nomogram using lasso regression and bootstrapping.
Main Results:
- Seizures occurred in 65.9% of acute JE cases.
- The incidence of PEE was 14.7%, with cumulative rates increasing over time.
- Independent risk factors for PEE included seizure number >5, status epilepticus, and coma.
- A validated nomogram demonstrated good predictive performance.
Conclusions:
- Seizures are a common manifestation and sequela of JE in children.
- The developed nomogram enables early PEE risk prediction.
- Early prediction facilitates timely intervention, potentially improving long-term outcomes.
Background:
We aimed to study seizure characteristics during the acute phase of Japanese encephalitis (JE) in children, determine the risk factors of postencephalitic epilepsy (PEE), establish a risk prediction model for the disease, and construct a nomogram to visualize the model.
Methods:
We retrospectively analyzed the clinical data and follow-up results of 328 children with JE who were hospitalized between January 2011 and December 2020. Risk factors were screened using univariable analysis, a predictive model was built using binary logistic analysis, lasso regression was used for variable screening, and a nomogram was developed.
Results:
Of the 328 children with JE enrolled in the study, 216 (65.9%, 216/328) had seizures in the acute phase. The incidence of PEE was 14.7% (39/264), The cumulative percentages of PEE after discharge was 10.6% (28/264)at 6 months, which increased to 13.6%(36/264)at 3 years. 38.5% of patients with PEE had generalized onset seizures, and 17.9% had focal motor seizures. Univariable analysis revealed that 22 clinical indicators were related to the PPE; Multivariable analysis identified seizure number >5 (OR (95%CI) = 3.013 (1.046-8.676), P = 0.041), status epilepticus (OR (95%CI) = 3.918 (1.212-12.669), P = 0.023), and Coma (OR (95%CI) = 22.495 (8.686-58.285), P<0.001) as independent risk factors for PEE. The risk prediction model: ln(p/1p)= -3.533 + 1.103 × (seizures number > 5) +1.366 × (status epilepticus) + 3.113 × (Coma) was developed, and a nomogram was constructed. The area under the ROC curve (AUC), calibration plot, and Hosmer-Lemeshow test showed that the model had good discrimination and calibration. Ordinary bootstrapping was used for internal validation, and the predictive results of the original and test sets were consistent.
Conclusions:
Seizure is a common manifestation during acute encephalitis and sequelae in children with JE. The nomogram constructed in this study could be used for early prediction, and could facilitate early intervention.
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